Multidataset Independent Subspace Analysis with Application to Multimodal Fusion
This work addresses the challenge of integrating diverse datasets for complex systems like brain imaging, offering a flexible approach for multimodal information fusion, though it appears incremental as an extension of existing subspace models.
The authors tackled the problem of combining multiple heterogeneous datasets to uncover shared and unique variability by introducing multidataset independent subspace analysis (MISA), a method that generalizes existing techniques like ICA and IVA, enabling robust multimodal fusion even in low-data or noisy conditions.
In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce. Seldom do these models combine the rich diversity of information available in multiple datasets. Multidatasets, on the other hand, yield joint solutions otherwise unavailable in isolation, with a potential for pivotal insights into complex systems. To take advantage of the complex multidimensional subspace structures that capture underlying modes of shared and unique variability across and within datasets, we present a direct, principled approach to multidataset combination. We design a new method called multidataset independent subspace analysis (MISA) that leverages joint information from multiple heterogeneous datasets in a flexible and synergistic fashion. Methodological innovations exploiting the Kotz distribution for subspace modeling in conjunction with a novel combinatorial optimization for evasion of local minima enable MISA to produce a robust generalization of independent component analysis (ICA), independent vector analysis (IVA), and independent subspace analysis (ISA) in a single unified model. We highlight the utility of MISA for multimodal information fusion, including sample-poor regimes and low signal-to-noise ratio scenarios, promoting novel applications in both unimodal and multimodal brain imaging data.